Abstractive summarization systems generate new phrases that express a text by using as few words as possible.

Translation by machines to perfectly transform text into another language.

Writing sentences based on structured data is also called Data-to-Text Generation.

Predicting the next word that is appropriate in the context.

Word Representations with the ability to incorporate context.

Multi-dimensional meaning representations of a word are reduced to a level of N dimensions, so the vectors can be used for similarity measures.

Estimating the degree of similarity between the semantic representation of two documents.

Measuring the syntax similarity or semantic word similarity by a specific distance calculation.

Defining lexical databases which consist of concepts that are described and interlinked by means of conceptual-semantic and lexical relations.

Finding text that is exceptionally far from the mainstream text.

Quantifying the deviation of the occurrence of words beyond the expected variability, and defining above what threshold you call this a trend.

Dividing a set of vectorized documents into N unsupervised topics by determining how similar vectors for a specific topic should be, and how many topics should be distinguished.

Extracting the most relevant sentences from a text works in the same way as Keyword extraction.

Providing the most relevant words from a document.

A specific sub-solution of Text Classification is Multi-Label Multi-Class Text Classification.

Assigning tags or categories to text according to its content. It is the broader task where Intent, Sentiment and Spam classification are part of.

Understanding the user’s intent and giving the correct responses.

Detecting the overall attitude expressed within a text in an imperative need to standardize the measurement of human sentiment and affective meaning.

ISPs continuously need to improve on detecting and filtering spam out.

Monitoring your Language Model might give you the feedback to further improve on performance and usage.

Deploying your Language Model might be a recurrent building block fro DevOps in a larger pipeline.